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121 lines
4.5 KiB
Markdown
121 lines
4.5 KiB
Markdown
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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# AltCLIP
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[AltCLIP](https://huggingface.co/papers/2211.06679v2) replaces the [CLIP](./clip) text encoder with a multilingual XLM-R encoder and aligns image and text representations with teacher learning and contrastive learning.
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You can find all the original AltCLIP checkpoints under the [AltClip](https://huggingface.co/collections/BAAI/alt-clip-diffusion-66987a97de8525205f1221bf) collection.
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> [!TIP]
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> Click on the AltCLIP models in the right sidebar for more examples of how to apply AltCLIP to different tasks.
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The examples below demonstrates how to calculate similarity scores between an image and one or more captions with the [`AutoModel`] class.
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<hfoptions id="usage">
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<hfoption id="AutoModel">
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```python
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import torch
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import requests
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from PIL import Image
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from transformers import AltCLIPModel, AltCLIPProcessor
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model = AltCLIPModel.from_pretrained("BAAI/AltCLIP", torch_dtype=torch.bfloat16)
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processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
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outputs = model(**inputs)
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logits_per_image = outputs.logits_per_image # this is the image-text similarity score
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probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
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labels = ["a photo of a cat", "a photo of a dog"]
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for label, prob in zip(labels, probs[0]):
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print(f"{label}: {prob.item():.4f}")
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```
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</hfoption>
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</hfoptions>
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Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
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The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
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```python
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# !pip install torchao
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import torch
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import requests
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from PIL import Image
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from transformers import AltCLIPModel, AltCLIPProcessor, TorchAoConfig
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model = AltCLIPModel.from_pretrained(
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"BAAI/AltCLIP",
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quantization_config=TorchAoConfig("int4_weight_only", group_size=128),
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torch_dtype=torch.bfloat16,
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)
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processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
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outputs = model(**inputs)
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logits_per_image = outputs.logits_per_image # this is the image-text similarity score
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probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
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labels = ["a photo of a cat", "a photo of a dog"]
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for label, prob in zip(labels, probs[0]):
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print(f"{label}: {prob.item():.4f}")
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```
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## Notes
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- AltCLIP uses bidirectional attention instead of causal attention and it uses the `[CLS]` token in XLM-R to represent a text embedding.
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- Use [`CLIPImageProcessor`] to resize (or rescale) and normalize images for the model.
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- [`AltCLIPProcessor`] combines [`CLIPImageProcessor`] and [`XLMRobertaTokenizer`] into a single instance to encode text and prepare images.
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## AltCLIPConfig
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[[autodoc]] AltCLIPConfig
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## AltCLIPTextConfig
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[[autodoc]] AltCLIPTextConfig
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## AltCLIPVisionConfig
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[[autodoc]] AltCLIPVisionConfig
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## AltCLIPModel
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[[autodoc]] AltCLIPModel
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## AltCLIPTextModel
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[[autodoc]] AltCLIPTextModel
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## AltCLIPVisionModel
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[[autodoc]] AltCLIPVisionModel
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## AltCLIPProcessor
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[[autodoc]] AltCLIPProcessor
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